store = {}
store['args']={'batch_size': 64, 'scoring_batch_size': 1000, 'test_batch_size': 1000, 'validation_set_size': 1000, 'early_stopping_patience': 3, 'epochs': 30, 'epoch_samples': 5056, 'num_inference_samples': 20, 'available_sample_k': 10, 'num_iterations': 100, 'no_cuda': False, 'name': 'bald_20_1023109', 'seed': 1023109, 'log_interval': 10, 'type': 'AcquisitionFunction.bald'}
store['iterations']=[]
store['initial_samples']=[23023, 25757, 15265, 29952, 42683, 7796, 13859, 3844, 11767, 23276, 13457, 6527, 24220, 29838, 2576, 18127, 32907, 17804, 39834, 11353]
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6055, 'nll': 2.6330612897872925}, 'chosen_samples': [49493, 35484, 20399, 11451, 17197, 12999, 25251, 53528, 48133, 29794], 'chosen_samples_score': ['1.1212702', '1.1345508', '1.182028', '1.1800346', '1.269969', '1.2107935', '1.2092358', '1.2026047', '1.1701272', '1.1827958']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6484, 'nll': 2.249552917480469}, 'chosen_samples': [37072, 42064, 31926, 12631, 54603, 43084, 49117, 10264, 59602, 44501], 'chosen_samples_score': ['1.0667853', '1.0671213', '1.0727677', '1.0741689', '1.078855', '1.081366', '1.092648', '1.1102993', '1.1110139', '1.1392722']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7562, 'nll': 1.381036001443863}, 'chosen_samples': [46255, 29595, 29871, 31801, 50106, 20944, 55782, 47885, 5137, 56564], 'chosen_samples_score': ['0.9994553', '1.0200746', '1.0233364', '1.0643785', '1.1208473', '1.0283545', '1.0217319', '1.0389256', '1.1302984', '1.0731375']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7785, 'nll': 1.2030242800712585}, 'chosen_samples': [26511, 30470, 15773, 22772, 12903, 25462, 58577, 40084, 51869, 38567], 'chosen_samples_score': ['0.9438293', '0.9453559', '0.9612535', '0.95971036', '0.96817726', '0.98468965', '1.0180767', '1.025574', '0.9888842', '0.98804444']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7723, 'nll': 1.1713430166244507}, 'chosen_samples': [48525, 46400, 12263, 8691, 20388, 42477, 42467, 16888, 16692, 18007], 'chosen_samples_score': ['0.93694425', '0.95115435', '0.9422801', '0.9593368', '0.9779111', '0.97330225', '1.0040464', '1.0671229', '0.93921274', '0.94299746']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7947, 'nll': 1.0872035682201386}, 'chosen_samples': [26192, 14187, 12157, 46777, 53976, 48343, 58076, 5062, 14888, 33630], 'chosen_samples_score': ['0.8629629', '0.86787134', '0.8751561', '0.8902552', '0.94357926', '0.8955601', '0.9178426', '1.0232679', '0.99642426', '0.9059358']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8209, 'nll': 0.9640337586402893}, 'chosen_samples': [39663, 51180, 15562, 44952, 9948, 33182, 38817, 40466, 31736, 2000], 'chosen_samples_score': ['0.85778356', '0.8593996', '0.8611936', '0.8618049', '0.9496167', '0.93054825', '0.89298904', '0.93510765', '0.9653509', '0.9709233']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8118, 'nll': 0.9354460120201111}, 'chosen_samples': [13313, 54973, 28102, 31063, 36950, 41301, 58587, 54880, 39879, 41255], 'chosen_samples_score': ['0.73738873', '0.7948837', '0.7673381', '0.7440289', '0.7739614', '0.7685823', '0.7998288', '0.7401049', '0.7755682', '0.78314257']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8016, 'nll': 0.9367608547210693}, 'chosen_samples': [49505, 14125, 10229, 14043, 38115, 25993, 46953, 49673, 21355, 17870], 'chosen_samples_score': ['0.72188586', '0.7225195', '0.7301419', '0.733157', '0.7273268', '0.7343383', '0.74777764', '0.755408', '0.88190705', '0.73967665']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8275, 'nll': 0.885275399684906}, 'chosen_samples': [59314, 28267, 14649, 27646, 7168, 39376, 56191, 21421, 34737, 19774], 'chosen_samples_score': ['0.7749254', '0.7860293', '0.81490296', '0.81434625', '0.8410883', '0.8137515', '0.8122557', '0.82777', '0.8047999', '0.8818611']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8493, 'nll': 0.8152760148048401}, 'chosen_samples': [14004, 13830, 12089, 33812, 54074, 36337, 41951, 11202, 57985, 19942], 'chosen_samples_score': ['0.6877323', '0.703669', '0.70868677', '0.6907579', '0.7230687', '0.7811894', '0.75325453', '0.8480174', '0.73469377', '0.73370403']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.822, 'nll': 0.9107602000236511}, 'chosen_samples': [47965, 27503, 47741, 37430, 20473, 47914, 15679, 55513, 20476, 21532], 'chosen_samples_score': ['0.71121776', '0.71201485', '0.71588016', '0.7183759', '0.7335739', '0.743969', '0.7788495', '0.7833801', '0.8038206', '0.89188343']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.857, 'nll': 0.8177871733903885}, 'chosen_samples': [50509, 45917, 20328, 3070, 8978, 56649, 22256, 20057, 36760, 4797], 'chosen_samples_score': ['0.8285682', '0.8318302', '0.83701277', '0.84459555', '0.88241297', '0.860048', '0.8468523', '0.8597884', '0.8483155', '0.8575426']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8591, 'nll': 0.7447999149560929}, 'chosen_samples': [18324, 12768, 20150, 10210, 8958, 53656, 5129, 2030, 23104, 49543], 'chosen_samples_score': ['0.8680703', '0.8891196', '0.90041685', '1.0357394', '0.9102564', '0.9187538', '0.9320295', '0.9279612', '0.952171', '1.0624511']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.8875, 'nll': 0.5983656615018844}, 'chosen_samples': [19396, 13942, 40046, 13149, 54904, 34478, 5188, 47401, 42078, 10926], 'chosen_samples_score': ['0.9581999', '0.9591533', '1.0861096', '0.9873712', '0.9856241', '0.9607048', '0.9839312', '0.9606942', '0.98231435', '0.96370983']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9285, 'nll': 0.48190483152866365}, 'chosen_samples': [39022, 47513, 52095, 12514, 11777, 37437, 29662, 57972, 43575, 30149], 'chosen_samples_score': ['0.92088693', '0.92787254', '0.93024975', '0.9602259', '1.0513437', '0.93207586', '0.95773506', '0.9478425', '1.1098542', '0.96248096']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9102, 'nll': 0.5078453034162521}, 'chosen_samples': [41307, 17958, 16756, 20897, 38920, 49064, 13021, 1019, 49616, 6428], 'chosen_samples_score': ['0.9167313', '0.91764927', '0.92206925', '0.9254345', '0.9250523', '0.9191548', '0.9305887', '0.9339556', '0.94154066', '0.94313633']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.923, 'nll': 0.49639252573251724}, 'chosen_samples': [10244, 45800, 32323, 52319, 13093, 49364, 32002, 32047, 30139, 49202], 'chosen_samples_score': ['0.9955362', '0.99638426', '1.0362825', '1.1554546', '1.0925131', '1.0140029', '1.0072235', '1.0343399', '1.014456', '1.0448236']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9398, 'nll': 0.4274006590247154}, 'chosen_samples': [18487, 45576, 7160, 57208, 2542, 16823, 49890, 37373, 24533, 11482], 'chosen_samples_score': ['1.044289', '1.044336', '1.0443922', '1.0516702', '1.0575855', '1.0495099', '1.1064985', '1.092282', '1.0499873', '1.2411084']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9401, 'nll': 0.4193595305085182}, 'chosen_samples': [4955, 31706, 11611, 52934, 19089, 46878, 39480, 12986, 41489, 47870], 'chosen_samples_score': ['0.9534576', '0.9583134', '0.95986944', '0.9847711', '0.98656493', '1.0267391', '0.9794752', '0.99157387', '1.0817453', '0.9853086']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9454, 'nll': 0.42101875245571135}, 'chosen_samples': [57507, 12792, 57574, 57387, 44149, 35051, 11621, 6309, 14540, 45424], 'chosen_samples_score': ['0.9880124', '0.98980355', '0.9942639', '1.0094808', '1.0041909', '0.9957792', '1.0797795', '1.0080944', '1.0001422', '1.0168315']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9462, 'nll': 0.40052448511123656}, 'chosen_samples': [1642, 58535, 33162, 11708, 29132, 41035, 18240, 21880, 34946, 20169], 'chosen_samples_score': ['0.9802466', '0.98441744', '0.9846028', '0.98467314', '0.9982157', '1.001128', '1.0338004', '1.0203676', '1.062254', '1.019767']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9513, 'nll': 0.3976180195808411}, 'chosen_samples': [24668, 13242, 12399, 49692, 4005, 42428, 24990, 52582, 39239, 11536], 'chosen_samples_score': ['0.96456873', '0.96841675', '1.0330511', '0.98631203', '0.9698067', '0.9767766', '1.0395885', '1.0889232', '1.0554503', '1.0064979']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9502, 'nll': 0.3794922411441803}, 'chosen_samples': [8765, 36744, 37048, 1674, 53872, 20170, 54969, 34563, 37347, 52225], 'chosen_samples_score': ['0.9608347', '0.9628332', '0.97381306', '0.97456187', '0.9809731', '0.98694617', '0.98759305', '1.005522', '1.0802498', '1.1640626']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9445, 'nll': 0.40363703221082686}, 'chosen_samples': [23962, 59726, 24860, 41907, 19298, 40066, 9180, 3241, 1030, 12305], 'chosen_samples_score': ['0.91841406', '0.93582326', '0.9681265', '0.9382267', '0.93223524', '0.9242628', '0.9726976', '0.9389705', '0.95938057', '0.9398486']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9479, 'nll': 0.3965111836791039}, 'chosen_samples': [12663, 3030, 30915, 25159, 47951, 20550, 14690, 12702, 32276, 57403], 'chosen_samples_score': ['0.9770764', '1.0142969', '0.98929656', '0.9991157', '0.99747425', '0.9784888', '1.0208344', '1.0252919', '1.093406', '1.1397967']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9519, 'nll': 0.3836767941713333}, 'chosen_samples': [31738, 22561, 56662, 34188, 50149, 13760, 48681, 43692, 35406, 635], 'chosen_samples_score': ['0.90592617', '0.9067815', '0.9109569', '0.92965513', '0.93135864', '0.9321807', '0.94494593', '0.95214695', '0.9356266', '0.9544274']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9573, 'nll': 0.3674029514193535}, 'chosen_samples': [11630, 29886, 33484, 49487, 47597, 35864, 20036, 19868, 18150, 52006], 'chosen_samples_score': ['1.0132856', '1.013869', '1.015924', '1.0322704', '1.0360508', '1.0454917', '1.0208697', '1.0508909', '1.0168357', '1.0573435']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9598, 'nll': 0.32250746339559555}, 'chosen_samples': [16011, 37469, 57742, 36417, 25728, 17478, 4153, 7768, 11292, 17540], 'chosen_samples_score': ['0.8439644', '0.84657925', '0.8490338', '0.8556358', '0.86355865', '0.8642823', '0.8658329', '0.9248328', '1.0054431', '0.8691578']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9588, 'nll': 0.33287004828453065}, 'chosen_samples': [45602, 42337, 42415, 11364, 25300, 26358, 22083, 2108, 40530, 8353], 'chosen_samples_score': ['0.95040816', '0.95494395', '0.96617097', '0.9551433', '0.9688375', '1.0538473', '1.0279691', '0.97873735', '1.0697854', '0.9760416']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9546, 'nll': 0.3526756316423416}, 'chosen_samples': [28374, 8934, 59344, 27458, 14815, 22497, 54030, 52688, 59731, 39668], 'chosen_samples_score': ['0.88456744', '0.8907032', '0.8860075', '0.8983162', '0.89862317', '0.90610623', '0.92101043', '0.8992291', '0.9405566', '0.933536']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.956, 'nll': 0.33554158061742784}, 'chosen_samples': [32926, 394, 35694, 7599, 18598, 3810, 26444, 14062, 47068, 33338], 'chosen_samples_score': ['0.9223901', '0.9257768', '0.9304681', '0.930671', '0.9345431', '1.0547907', '1.054956', '0.9727384', '0.96863705', '0.9661374']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9605, 'nll': 0.3318572074174881}, 'chosen_samples': [21636, 9448, 44172, 44350, 8867, 17296, 43588, 37044, 32880, 47140], 'chosen_samples_score': ['0.8610346', '0.86228395', '0.9101957', '0.9546146', '0.9266084', '0.9079191', '0.8805967', '0.9039737', '0.9909151', '0.9040528']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9655, 'nll': 0.3006756231188774}, 'chosen_samples': [41802, 14376, 47247, 40589, 4590, 24360, 43560, 42199, 42973, 7308], 'chosen_samples_score': ['0.95779145', '0.9579899', '0.980136', '0.98076826', '0.9691043', '1.0595152', '0.9645358', '0.98079973', '0.960892', '0.99315095']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.954, 'nll': 0.3547836631536484}, 'chosen_samples': [31345, 55244, 57195, 13259, 46734, 14746, 19188, 5295, 51618, 47479], 'chosen_samples_score': ['0.87927556', '0.8807963', '0.88588285', '0.8825912', '0.8870353', '0.9357745', '0.89419436', '1.0143378', '0.90689206', '0.89437544']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9667, 'nll': 0.2821087777614594}, 'chosen_samples': [31474, 4185, 43126, 41334, 49515, 12874, 30884, 21436, 48154, 47036], 'chosen_samples_score': ['0.89346915', '0.89649934', '0.8982525', '0.9170328', '0.9062929', '0.9223874', '0.924807', '0.9339533', '0.94325036', '0.9740895']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9688, 'nll': 0.27892853543162344}, 'chosen_samples': [3268, 15945, 55073, 44442, 49354, 15988, 10070, 22480, 46441, 21426], 'chosen_samples_score': ['0.9606065', '0.96204364', '0.9870821', '0.9963261', '1.0046295', '0.9751521', '1.0123923', '1.0166519', '1.0307041', '1.0283165']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9648, 'nll': 0.2830357953906059}, 'chosen_samples': [41464, 46368, 32776, 30011, 52808, 23788, 36686, 52800, 31252, 24424], 'chosen_samples_score': ['0.9605696', '0.96325207', '0.9787356', '0.987893', '0.9852107', '1.0075443', '1.0108223', '1.0016525', '1.0726931', '1.0292671']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9656, 'nll': 0.3042159005999565}, 'chosen_samples': [45056, 54542, 32190, 8849, 2381, 26460, 23140, 9340, 49987, 266], 'chosen_samples_score': ['0.93364054', '0.94375354', '0.9586584', '0.95549417', '0.9576607', '0.97020346', '0.97485113', '1.0188284', '1.044908', '1.0540935']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9671, 'nll': 0.29373374581336975}, 'chosen_samples': [31883, 38698, 30440, 49573, 37247, 38136, 28536, 47643, 16951, 50734], 'chosen_samples_score': ['0.89153975', '0.97025955', '0.90498424', '0.99158865', '0.92742246', '0.8981243', '1.0498478', '0.8934809', '0.9001937', '0.9181294']})
store['iterations'].append({'num_epochs': 14, 'test_metrics': {'accuracy': 0.9724, 'nll': 0.267070934176445}, 'chosen_samples': [38147, 40494, 56006, 9501, 14813, 33318, 29711, 56014, 24589, 28152], 'chosen_samples_score': ['0.93174046', '0.9539292', '0.9997166', '0.99848926', '1.0269449', '0.98122776', '0.9615596', '1.1551478', '1.0037327', '0.9363175']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9684, 'nll': 0.27606410831212996}, 'chosen_samples': [21842, 57718, 13714, 42734, 34304, 5896, 36818, 15450, 49153, 34660], 'chosen_samples_score': ['0.834576', '0.8364496', '0.8381383', '0.8569233', '0.8855871', '0.8893218', '0.893279', '0.88970053', '0.8959778', '0.9661598']})
store['iterations'].append({'num_epochs': 15, 'test_metrics': {'accuracy': 0.9692, 'nll': 0.2755230516195297}, 'chosen_samples': [3218, 15803, 50155, 12426, 10256, 22594, 33505, 46187, 38171, 45911], 'chosen_samples_score': ['0.9676581', '0.9685305', '0.97525275', '0.99683684', '1.004803', '1.0060393', '1.0543144', '1.0199313', '1.0254338', '1.1285534']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9723, 'nll': 0.27362397164106367}, 'chosen_samples': [51238, 8853, 50346, 41540, 34829, 12898, 34771, 32994, 1239, 19495], 'chosen_samples_score': ['0.87772363', '0.88214445', '0.8894763', '0.8861747', '0.89853024', '0.9424457', '1.0649881', '0.90667063', '0.9048512', '0.9568048']})
store['iterations'].append({'num_epochs': 15, 'test_metrics': {'accuracy': 0.9721, 'nll': 0.26539927572011945}, 'chosen_samples': [4530, 10982, 55368, 20110, 50505, 56494, 44753, 54994, 57768, 29286], 'chosen_samples_score': ['0.9910166', '0.99191004', '0.99666816', '0.99901575', '1.0111648', '1.054147', '1.0415068', '1.1046243', '1.0148857', '1.1870706']})
store['iterations'].append({'num_epochs': 14, 'test_metrics': {'accuracy': 0.9717, 'nll': 0.2664914593100548}, 'chosen_samples': [51394, 8693, 57380, 5247, 8228, 6418, 48102, 44364, 29294, 35205], 'chosen_samples_score': ['0.9436414', '0.94611347', '0.9484769', '0.946976', '0.9562082', '1.0477277', '0.9595382', '1.0309706', '0.9612306', '0.9762891']})
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store['iterations'].append({'num_epochs': 16, 'test_metrics': {'accuracy': 0.9812, 'nll': 0.2125127673149109}, 'chosen_samples': [26029, 41933, 58709, 47403, 30897, 588, 13031, 13831, 46088, 54], 'chosen_samples_score': ['0.79630286', '0.7973266', '0.8159057', '0.81272215', '0.8008445', '0.8728792', '0.80084836', '0.8516621', '0.8176589', '0.7997658']})
store['iterations'].append({'num_epochs': 19, 'test_metrics': {'accuracy': 0.9822, 'nll': 0.20085905566811563}, 'chosen_samples': [19430, 3814, 49662, 9384, 15068, 3798, 33752, 31197, 42503, 25910], 'chosen_samples_score': ['0.8318957', '0.83782154', '0.8603783', '0.8871545', '0.8885186', '0.94193137', '0.9017243', '0.96226984', '0.9921223', '0.894466']})
store['iterations'].append({'num_epochs': 22, 'test_metrics': {'accuracy': 0.9842, 'nll': 0.20232385993003846}, 'chosen_samples': [43256, 42526, 14828, 55475, 1448, 50572, 49012, 1682, 25803, 55194], 'chosen_samples_score': ['0.86195946', '0.884691', '0.8847831', '0.8872893', '0.8626969', '0.8633616', '0.88849473', '0.9688197', '0.97469723', '0.98254967']})
store['iterations'].append({'num_epochs': 14, 'test_metrics': {'accuracy': 0.9832, 'nll': 0.19913018196821214}, 'chosen_samples': [55792, 55438, 37552, 16755, 40874, 21900, 13922, 7886, 49501, 34785], 'chosen_samples_score': ['0.7603889', '0.7609918', '0.76271504', '0.7635141', '0.76892585', '0.78117895', '0.77619874', '0.8246889', '0.79697883', '0.8050543']})
store['iterations'].append({'num_epochs': 26, 'test_metrics': {'accuracy': 0.984, 'nll': 0.19155413433909416}, 'chosen_samples': [40660, 8918, 32519, 17739, 4634, 29360, 37962, 37696, 47949, 31884], 'chosen_samples_score': ['0.87451744', '0.8863796', '0.88883996', '0.8947877', '0.89560294', '0.8973483', '0.94211173', '1.1099784', '0.9273319', '0.9397296']})
